Contrastive adversarial knowledge distillation for deep model compression in time-series regression tasks. - Qing Xu 015; Zhenghua Chen; Mohamed Ragab 002; Chao Wang 003; Min Wu 008; Xiaoli Li 001 (Neurocomputing, 2022)

KDnet-RUL: A Knowledge Distillation Framework to Compress Deep Neural Networks for Machine Remaining Useful Life Prediction. - Qing Xu 015; Zhenghua Chen; Keyu Wu 002; Chao Wang 003; Min Wu 008; Xiaoli Li 001 (IEEE Trans. Ind. Electron., 2022)

Distilling Universal and Joint Knowledge for Cross-Domain Model Compression on Time Series Data. - Qing Xu 015; Min Wu 008; Xiaoli Li 001; Kezhi Mao; Zhenghua Chen (IJCAI, 2023)

A Hybrid Ensemble Deep Learning Approach for Early Prediction of Battery Remaining Useful Life. - Qing Xu 015; Min Wu 008; Edwin Khoo; Zhenghua Chen; Xiaoli Li 001 (IEEE CAA J. Autom. Sinica, 2023)

Reinforced Cross-Domain Knowledge Distillation on Time Series Data. - Qing Xu 015; Min Wu 008; Xiaoli Li 001; Kezhi Mao; Zhenghua Chen (NeurIPS, 2024)

Improve Knowledge Distillation via Label Revision and Data Selection. - Weichao Lan; Yiu-ming Cheung; Qing Xu 015; Buhua Liu; Zhikai Hu; Mengke Li 001; Zhenghua Chen (CoRR, 2024)

From Algorithm to Hardware: A Survey on Efficient and Safe Deployment of Deep Neural Networks. - Xue Geng; Zhe Wang 019; Chunyun Chen; Qing Xu 015; Kaixin Xu; Chao Jin 002; Manas Gupta; Xulei Yang; Zhenghua Chen; Mohamed M. Sabry Aly; Jie Lin 001; Min Wu 008; et al. (CoRR, 2024)

LLM-based Knowledge Pruning for Time Series Data Analytics on Edge-computing Devices. - Ruibing Jin; Qing Xu 015; Min Wu 008; Yuecong Xu; Dan Li; Xiaoli Li 001; Zhenghua Chen (CoRR, 2024)

Reinforced Knowledge Distillation for Time Series Regression. - Qing Xu 015; Keyu Wu 002; Min Wu 008; Kezhi Mao; Xiaoli Li 001; Zhenghua Chen (IEEE Trans. Artif. Intell., 2024)

From Algorithm to Hardware: A Survey on Efficient and Safe Deployment of Deep Neural Networks. - Xue Geng; Zhe Wang 019; Chunyun Chen; Qing Xu 015; Kaixin Xu; Jin Chao; Manas Gupta; Xulei Yang; Zhenghua Chen; Mohamed M. Sabry Aly; Jie Lin 001; Min Wu 008; et al. (IEEE Trans. Neural Networks Learn. Syst., 2025)